Overview

Dataset statistics

Number of variables24
Number of observations66541
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)< 0.1%
Total size in memory55.0 MiB
Average record size in memory867.1 B

Variable types

Text6
DateTime3
Unsupported1
Categorical7
Numeric7

Alerts

Taxes (GST) has constant value ""Constant
Dataset has 4 (< 0.1%) duplicate rowsDuplicates
Booking Price[SGD] is highly overall correlated with Hotel RatingHigh correlation
Hotel Rating is highly overall correlated with Booking Price[SGD]High correlation
No. Of People is highly overall correlated with RoomsHigh correlation
Rooms is highly overall correlated with No. Of PeopleHigh correlation
Time is an unsupported type, check if it needs cleaning or further analysisUnsupported
Discount has 2547 (3.8%) zerosZeros

Reproduction

Analysis started2024-02-28 13:58:11.508334
Analysis finished2024-02-28 13:58:34.704642
Duration23.2 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Distinct66536
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2024-02-28T13:58:35.028247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters598869
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66532 ?
Unique (%)> 99.9%

Sample

1st rowDDMY00001
2nd rowDDID00002
3rd rowDDSG00003
4th rowDDSG00004
5th rowDDKH00005
ValueCountFrequency (%)
ddvn34340 3
 
< 0.1%
ddsg22537 2
 
< 0.1%
ddkh50738 2
 
< 0.1%
ddmy00008 2
 
< 0.1%
ddvn00031 1
 
< 0.1%
ddkh00034 1
 
< 0.1%
ddvn00033 1
 
< 0.1%
ddsg00017 1
 
< 0.1%
ddsg00003 1
 
< 0.1%
ddsg00004 1
 
< 0.1%
Other values (66526) 66526
> 99.9%
2024-02-28T13:58:35.790364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 145068
24.2%
2 37016
 
6.2%
3 37016
 
6.2%
0 37016
 
6.2%
1 37014
 
6.2%
4 37008
 
6.2%
5 36943
 
6.2%
6 32978
 
5.5%
7 25906
 
4.3%
8 25905
 
4.3%
Other values (13) 146999
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 332705
55.6%
Uppercase Letter 266164
44.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 145068
54.5%
H 24507
 
9.2%
T 12170
 
4.6%
M 12050
 
4.5%
Y 12050
 
4.5%
G 12037
 
4.5%
S 12037
 
4.5%
I 11986
 
4.5%
P 6383
 
2.4%
V 5961
 
2.2%
Other values (3) 11915
 
4.5%
Decimal Number
ValueCountFrequency (%)
2 37016
11.1%
3 37016
11.1%
0 37016
11.1%
1 37014
11.1%
4 37008
11.1%
5 36943
11.1%
6 32978
9.9%
7 25906
7.8%
8 25905
7.8%
9 25903
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 332705
55.6%
Latin 266164
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 145068
54.5%
H 24507
 
9.2%
T 12170
 
4.6%
M 12050
 
4.5%
Y 12050
 
4.5%
G 12037
 
4.5%
S 12037
 
4.5%
I 11986
 
4.5%
P 6383
 
2.4%
V 5961
 
2.2%
Other values (3) 11915
 
4.5%
Common
ValueCountFrequency (%)
2 37016
11.1%
3 37016
11.1%
0 37016
11.1%
1 37014
11.1%
4 37008
11.1%
5 36943
11.1%
6 32978
9.9%
7 25906
7.8%
8 25905
7.8%
9 25903
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 598869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 145068
24.2%
2 37016
 
6.2%
3 37016
 
6.2%
0 37016
 
6.2%
1 37014
 
6.2%
4 37008
 
6.2%
5 36943
 
6.2%
6 32978
 
5.5%
7 25906
 
4.3%
8 25905
 
4.3%
Other values (13) 146999
24.5%
Distinct3652
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size520.0 KiB
Minimum2010-01-01 00:00:00
Maximum2019-12-31 00:00:00
2024-02-28T13:58:36.073483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:36.276064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Time
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.0 MiB
Distinct66536
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-02-28T13:58:36.778503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters465787
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66532 ?
Unique (%)> 99.9%

Sample

1st rowMY00001
2nd rowID00001
3rd rowSG00001
4th rowSG00002
5th rowKH00001
ValueCountFrequency (%)
vn03010 3
 
< 0.1%
sg04062 2
 
< 0.1%
kh04520 2
 
< 0.1%
my00002 2
 
< 0.1%
vn00003 1
 
< 0.1%
kh00004 1
 
< 0.1%
vn00004 1
 
< 0.1%
sg00005 1
 
< 0.1%
sg00001 1
 
< 0.1%
sg00002 1
 
< 0.1%
Other values (66526) 66526
> 99.9%
2024-02-28T13:58:37.667037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 89500
19.2%
1 39342
 
8.4%
2 27298
 
5.9%
3 27019
 
5.8%
4 26927
 
5.8%
5 26822
 
5.8%
H 24506
 
5.3%
6 24279
 
5.2%
7 23883
 
5.1%
8 23873
 
5.1%
Other values (13) 132338
28.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 332705
71.4%
Uppercase Letter 133082
 
28.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 24506
18.4%
T 12170
9.1%
Y 12050
9.1%
M 12050
9.1%
G 12037
9.0%
S 12037
9.0%
D 11987
9.0%
I 11986
9.0%
P 6383
 
4.8%
V 5961
 
4.5%
Other values (3) 11915
9.0%
Decimal Number
ValueCountFrequency (%)
0 89500
26.9%
1 39342
11.8%
2 27298
 
8.2%
3 27019
 
8.1%
4 26927
 
8.1%
5 26822
 
8.1%
6 24279
 
7.3%
7 23883
 
7.2%
8 23873
 
7.2%
9 23762
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 332705
71.4%
Latin 133082
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 24506
18.4%
T 12170
9.1%
Y 12050
9.1%
M 12050
9.1%
G 12037
9.0%
S 12037
9.0%
D 11987
9.0%
I 11986
9.0%
P 6383
 
4.8%
V 5961
 
4.5%
Other values (3) 11915
9.0%
Common
ValueCountFrequency (%)
0 89500
26.9%
1 39342
11.8%
2 27298
 
8.2%
3 27019
 
8.1%
4 26927
 
8.1%
5 26822
 
8.1%
6 24279
 
7.3%
7 23883
 
7.2%
8 23873
 
7.2%
9 23762
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89500
19.2%
1 39342
 
8.4%
2 27298
 
5.9%
3 27019
 
5.8%
4 26927
 
5.8%
5 26822
 
5.8%
H 24506
 
5.3%
6 24279
 
5.2%
7 23883
 
5.1%
8 23873
 
5.1%
Other values (13) 132338
28.4%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
Female
33388 
Male
33153 

Length

Max length6
Median length6
Mean length5.0035317
Min length4

Characters and Unicode

Total characters332940
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 33388
50.2%
Male 33153
49.8%

Length

2024-02-28T13:58:38.085956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:58:38.333330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
female 33388
50.2%
male 33153
49.8%

Most occurring characters

ValueCountFrequency (%)
e 99929
30.0%
a 66541
20.0%
l 66541
20.0%
F 33388
 
10.0%
m 33388
 
10.0%
M 33153
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 266399
80.0%
Uppercase Letter 66541
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 99929
37.5%
a 66541
25.0%
l 66541
25.0%
m 33388
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
F 33388
50.2%
M 33153
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 332940
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 99929
30.0%
a 66541
20.0%
l 66541
20.0%
F 33388
 
10.0%
m 33388
 
10.0%
M 33153
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 99929
30.0%
a 66541
20.0%
l 66541
20.0%
F 33388
 
10.0%
m 33388
 
10.0%
M 33153
 
10.0%

Age
Real number (ℝ)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.436182
Minimum-5
Maximum58
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size520.0 KiB
2024-02-28T13:58:38.599523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile21
Q128
median38
Q348
95-th percentile57
Maximum58
Range63
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.557562
Coefficient of variation (CV)0.30069486
Kurtosis-1.199354
Mean38.436182
Median Absolute Deviation (MAD)10
Skewness0.010175915
Sum2557582
Variance133.57725
MonotonicityNot monotonic
2024-02-28T13:58:38.807524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
22 1776
 
2.7%
56 1754
 
2.6%
25 1742
 
2.6%
28 1735
 
2.6%
44 1732
 
2.6%
47 1726
 
2.6%
46 1723
 
2.6%
19 1722
 
2.6%
58 1721
 
2.6%
33 1715
 
2.6%
Other values (31) 49195
73.9%
ValueCountFrequency (%)
-5 1
 
< 0.1%
19 1722
2.6%
20 1601
2.4%
21 1645
2.5%
22 1776
2.7%
23 1630
2.4%
24 1645
2.5%
25 1742
2.6%
26 1651
2.5%
27 1647
2.5%
ValueCountFrequency (%)
58 1721
2.6%
57 1611
2.4%
56 1754
2.6%
55 1617
2.4%
54 1671
2.5%
53 1598
2.4%
52 1607
2.4%
51 1659
2.5%
50 1651
2.5%
49 1594
2.4%

Origin Country
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Thailand
12170 
Malaysia
12050 
Singapore
12038 
Indonesia
11986 
Philippines
6383 
Other values (2)
11914 

Length

Max length11
Median length9
Mean length8.5592342
Min length7

Characters and Unicode

Total characters569540
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMalaysia
2nd rowIndonesia
3rd rowSingapore
4th rowSingapore
5th rowCambodia

Common Values

ValueCountFrequency (%)
Thailand 12170
18.3%
Malaysia 12050
18.1%
Singapore 12038
18.1%
Indonesia 11986
18.0%
Philippines 6383
9.6%
Vietnam 5961
9.0%
Cambodia 5953
8.9%

Length

2024-02-28T13:58:39.092228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:58:39.401650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thailand 12170
18.3%
malaysia 12050
18.1%
singapore 12038
18.1%
indonesia 11986
18.0%
philippines 6383
9.6%
vietnam 5961
9.0%
cambodia 5953
8.9%

Most occurring characters

ValueCountFrequency (%)
a 102381
18.0%
i 79307
13.9%
n 60524
10.6%
e 36368
 
6.4%
l 30603
 
5.4%
s 30419
 
5.3%
d 30109
 
5.3%
o 29977
 
5.3%
p 24804
 
4.4%
h 18553
 
3.3%
Other values (13) 126495
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 502999
88.3%
Uppercase Letter 66541
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 102381
20.4%
i 79307
15.8%
n 60524
12.0%
e 36368
 
7.2%
l 30603
 
6.1%
s 30419
 
6.0%
d 30109
 
6.0%
o 29977
 
6.0%
p 24804
 
4.9%
h 18553
 
3.7%
Other values (6) 59954
11.9%
Uppercase Letter
ValueCountFrequency (%)
T 12170
18.3%
M 12050
18.1%
S 12038
18.1%
I 11986
18.0%
P 6383
9.6%
V 5961
9.0%
C 5953
8.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 569540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 102381
18.0%
i 79307
13.9%
n 60524
10.6%
e 36368
 
6.4%
l 30603
 
5.4%
s 30419
 
5.3%
d 30109
 
5.3%
o 29977
 
5.3%
p 24804
 
4.4%
h 18553
 
3.3%
Other values (13) 126495
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 569540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 102381
18.0%
i 79307
13.9%
n 60524
10.6%
e 36368
 
6.4%
l 30603
 
5.4%
s 30419
 
5.3%
d 30109
 
5.3%
o 29977
 
5.3%
p 24804
 
4.4%
h 18553
 
3.3%
Other values (13) 126495
22.2%

State
Text

Distinct219
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-02-28T13:58:39.939897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.4767887
Min length3

Characters and Unicode

Total characters497513
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohor
2nd rowCiawi
3rd rowCentral
4th rowNorth
5th rowPhnom Trop
ValueCountFrequency (%)
central 4821
 
5.7%
west 2617
 
3.1%
sarawak 1884
 
2.2%
johor 1863
 
2.2%
penang 1847
 
2.2%
north 1799
 
2.1%
selangor 1793
 
2.1%
thani 1757
 
2.1%
north-east 1461
 
1.7%
chonburi 1350
 
1.6%
Other values (262) 63007
74.8%
2024-02-28T13:58:40.764865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 80820
16.2%
n 45842
 
9.2%
e 29429
 
5.9%
o 29161
 
5.9%
r 28803
 
5.8%
h 25047
 
5.0%
t 24806
 
5.0%
g 19896
 
4.0%
i 18063
 
3.6%
u 17867
 
3.6%
Other values (40) 177779
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 392734
78.9%
Uppercase Letter 85660
 
17.2%
Space Separator 17658
 
3.5%
Dash Punctuation 1461
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 80820
20.6%
n 45842
11.7%
e 29429
 
7.5%
o 29161
 
7.4%
r 28803
 
7.3%
h 25047
 
6.4%
t 24806
 
6.3%
g 19896
 
5.1%
i 18063
 
4.6%
u 17867
 
4.5%
Other values (14) 73000
18.6%
Uppercase Letter
ValueCountFrequency (%)
S 13064
15.3%
C 10127
11.8%
P 10118
11.8%
T 9002
10.5%
N 7380
8.6%
K 5443
 
6.4%
B 4565
 
5.3%
M 4318
 
5.0%
E 3100
 
3.6%
L 2863
 
3.3%
Other values (14) 15680
18.3%
Space Separator
ValueCountFrequency (%)
17658
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 478394
96.2%
Common 19119
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 80820
16.9%
n 45842
 
9.6%
e 29429
 
6.2%
o 29161
 
6.1%
r 28803
 
6.0%
h 25047
 
5.2%
t 24806
 
5.2%
g 19896
 
4.2%
i 18063
 
3.8%
u 17867
 
3.7%
Other values (38) 158660
33.2%
Common
ValueCountFrequency (%)
17658
92.4%
- 1461
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 497513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 80820
16.2%
n 45842
 
9.2%
e 29429
 
5.9%
o 29161
 
5.9%
r 28803
 
5.8%
h 25047
 
5.0%
t 24806
 
5.0%
g 19896
 
4.0%
i 18063
 
3.6%
u 17867
 
3.6%
Other values (40) 177779
35.7%
Distinct228
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2024-02-28T13:58:41.377429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length9.3061721
Min length4

Characters and Unicode

Total characters619242
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIskandar Puteri
2nd rowWest Java
3rd rowRochor
4th rowYishun
5th rowPursat
ValueCountFrequency (%)
jakarta 3519
 
3.3%
java 3082
 
2.9%
west 2425
 
2.3%
binh 1700
 
1.6%
east 1605
 
1.5%
central 1512
 
1.4%
papua 1458
 
1.4%
nakhon 1281
 
1.2%
kampong 1112
 
1.0%
kuching 985
 
0.9%
Other values (284) 87950
82.5%
2024-02-28T13:58:42.273776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 105023
17.0%
n 46782
 
7.6%
40088
 
6.5%
e 34273
 
5.5%
o 30788
 
5.0%
r 30421
 
4.9%
t 30245
 
4.9%
i 29229
 
4.7%
h 26787
 
4.3%
g 23069
 
3.7%
Other values (42) 222537
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 471537
76.1%
Uppercase Letter 107080
 
17.3%
Space Separator 40088
 
6.5%
Dash Punctuation 537
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 105023
22.3%
n 46782
9.9%
e 34273
 
7.3%
o 30788
 
6.5%
r 30421
 
6.5%
t 30245
 
6.4%
i 29229
 
6.2%
h 26787
 
5.7%
g 23069
 
4.9%
u 22760
 
4.8%
Other values (15) 92160
19.5%
Uppercase Letter
ValueCountFrequency (%)
S 12758
11.9%
P 10874
10.2%
K 10104
 
9.4%
J 9634
 
9.0%
T 9035
 
8.4%
C 7855
 
7.3%
B 6973
 
6.5%
M 5500
 
5.1%
N 5259
 
4.9%
W 3552
 
3.3%
Other values (14) 25536
23.8%
Dash Punctuation
ValueCountFrequency (%)
- 462
86.0%
‑ 75
 
14.0%
Space Separator
ValueCountFrequency (%)
40088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 578617
93.4%
Common 40625
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 105023
18.2%
n 46782
 
8.1%
e 34273
 
5.9%
o 30788
 
5.3%
r 30421
 
5.3%
t 30245
 
5.2%
i 29229
 
5.1%
h 26787
 
4.6%
g 23069
 
4.0%
u 22760
 
3.9%
Other values (39) 199240
34.4%
Common
ValueCountFrequency (%)
40088
98.7%
- 462
 
1.1%
‑ 75
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 618943
> 99.9%
Latin 1 Sup 224
 
< 0.1%
Punctuation 75
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 105023
17.0%
n 46782
 
7.6%
40088
 
6.5%
e 34273
 
5.5%
o 30788
 
5.0%
r 30421
 
4.9%
t 30245
 
4.9%
i 29229
 
4.7%
h 26787
 
4.3%
g 23069
 
3.7%
Other values (40) 222238
35.9%
Latin 1 Sup
ValueCountFrequency (%)
ñ 224
100.0%
Punctuation
ValueCountFrequency (%)
‑ 75
100.0%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
New Zealand
 
3448
Nepal
 
3446
Egypt
 
3423
Colombia
 
3408
China
 
3391
Other values (15)
49425 

Length

Max length11
Median length8
Mean length6.2133572
Min length4

Characters and Unicode

Total characters413443
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDenmark
2nd rowColombia
3rd rowGermany
4th rowCanada
5th rowKenya

Common Values

ValueCountFrequency (%)
New Zealand 3448
 
5.2%
Nepal 3446
 
5.2%
Egypt 3423
 
5.1%
Colombia 3408
 
5.1%
China 3391
 
5.1%
Denmark 3375
 
5.1%
Israel 3356
 
5.0%
France 3346
 
5.0%
Iceland 3344
 
5.0%
Italy 3342
 
5.0%
Other values (10) 32662
49.1%

Length

2024-02-28T13:58:42.639385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 3448
 
4.9%
zealand 3448
 
4.9%
nepal 3446
 
4.9%
egypt 3423
 
4.9%
colombia 3408
 
4.9%
china 3391
 
4.8%
denmark 3375
 
4.8%
israel 3356
 
4.8%
france 3346
 
4.8%
iceland 3344
 
4.8%
Other values (11) 36004
51.4%

Most occurring characters

ValueCountFrequency (%)
a 73226
17.7%
e 40135
 
9.7%
n 39803
 
9.6%
l 30166
 
7.3%
r 23107
 
5.6%
d 19995
 
4.8%
i 19853
 
4.8%
I 19821
 
4.8%
y 13299
 
3.2%
C 10130
 
2.5%
Other values (25) 123908
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 340006
82.2%
Uppercase Letter 69989
 
16.9%
Space Separator 3448
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 73226
21.5%
e 40135
11.8%
n 39803
11.7%
l 30166
8.9%
r 23107
 
6.8%
d 19995
 
5.9%
i 19853
 
5.8%
y 13299
 
3.9%
p 10124
 
3.0%
m 10084
 
3.0%
Other values (12) 60214
17.7%
Uppercase Letter
ValueCountFrequency (%)
I 19821
28.3%
C 10130
14.5%
N 6894
 
9.9%
M 6522
 
9.3%
Z 3448
 
4.9%
E 3423
 
4.9%
D 3375
 
4.8%
F 3346
 
4.8%
G 3301
 
4.7%
J 3255
 
4.7%
Other values (2) 6474
 
9.3%
Space Separator
ValueCountFrequency (%)
3448
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 409995
99.2%
Common 3448
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 73226
17.9%
e 40135
 
9.8%
n 39803
 
9.7%
l 30166
 
7.4%
r 23107
 
5.6%
d 19995
 
4.9%
i 19853
 
4.8%
I 19821
 
4.8%
y 13299
 
3.2%
C 10130
 
2.5%
Other values (24) 120460
29.4%
Common
ValueCountFrequency (%)
3448
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 413443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 73226
17.7%
e 40135
 
9.7%
n 39803
 
9.6%
l 30166
 
7.3%
r 23107
 
5.6%
d 19995
 
4.8%
i 19853
 
4.8%
I 19821
 
4.8%
y 13299
 
3.2%
C 10130
 
2.5%
Other values (25) 123908
30.0%
Distinct120
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-02-28T13:58:43.131836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.9708901
Min length3

Characters and Unicode

Total characters463850
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHorsens
2nd rowMedellin
3rd rowMunich
4th rowMontreal
5th rowNairobi
ValueCountFrequency (%)
city 1094
 
1.6%
hamilton 1084
 
1.6%
bello 646
 
0.9%
horizonte 635
 
0.9%
belo 635
 
0.9%
dublin 628
 
0.9%
auckland 624
 
0.9%
shanghai 620
 
0.9%
toronto 608
 
0.9%
haifa 607
 
0.9%
Other values (115) 62710
89.7%
2024-02-28T13:58:43.835071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 62260
 
13.4%
o 35034
 
7.6%
e 33275
 
7.2%
r 33046
 
7.1%
i 31744
 
6.8%
n 29936
 
6.5%
l 24615
 
5.3%
u 18975
 
4.1%
t 16942
 
3.7%
h 13836
 
3.0%
Other values (40) 164187
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 390609
84.2%
Uppercase Letter 69891
 
15.1%
Space Separator 3350
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 62260
15.9%
o 35034
 
9.0%
e 33275
 
8.5%
r 33046
 
8.5%
i 31744
 
8.1%
n 29936
 
7.7%
l 24615
 
6.3%
u 18975
 
4.9%
t 16942
 
4.3%
h 13836
 
3.5%
Other values (16) 90946
23.3%
Uppercase Letter
ValueCountFrequency (%)
M 7102
 
10.2%
B 6324
 
9.0%
H 6095
 
8.7%
T 5175
 
7.4%
A 5059
 
7.2%
N 4370
 
6.3%
C 4363
 
6.2%
K 3890
 
5.6%
S 3867
 
5.5%
G 3767
 
5.4%
Other values (13) 19879
28.4%
Space Separator
ValueCountFrequency (%)
3350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 460500
99.3%
Common 3350
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 62260
 
13.5%
o 35034
 
7.6%
e 33275
 
7.2%
r 33046
 
7.2%
i 31744
 
6.9%
n 29936
 
6.5%
l 24615
 
5.3%
u 18975
 
4.1%
t 16942
 
3.7%
h 13836
 
3.0%
Other values (39) 160837
34.9%
Common
ValueCountFrequency (%)
3350
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 463306
99.9%
Latin 1 Sup 544
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 62260
 
13.4%
o 35034
 
7.6%
e 33275
 
7.2%
r 33046
 
7.1%
i 31744
 
6.9%
n 29936
 
6.5%
l 24615
 
5.3%
u 18975
 
4.1%
t 16942
 
3.7%
h 13836
 
3.0%
Other values (39) 163643
35.3%
Latin 1 Sup
ValueCountFrequency (%)
í 544
100.0%

No. Of People
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.995777
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.0 KiB
2024-02-28T13:58:44.094566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0000744
Coefficient of variation (CV)0.50054706
Kurtosis-1.2494204
Mean3.995777
Median Absolute Deviation (MAD)2
Skewness0.0068885372
Sum265883
Variance4.0002978
MonotonicityNot monotonic
2024-02-28T13:58:44.441689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 9593
14.4%
4 9554
14.4%
7 9548
14.3%
3 9541
14.3%
1 9477
14.2%
5 9423
14.2%
6 9405
14.1%
ValueCountFrequency (%)
1 9477
14.2%
2 9593
14.4%
3 9541
14.3%
4 9554
14.4%
5 9423
14.2%
6 9405
14.1%
7 9548
14.3%
ValueCountFrequency (%)
7 9548
14.3%
6 9405
14.1%
5 9423
14.2%
4 9554
14.4%
3 9541
14.3%
2 9593
14.4%
1 9477
14.2%
Distinct3747
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size520.0 KiB
Minimum2010-01-02 00:00:00
Maximum2020-04-09 00:00:00
2024-02-28T13:58:44.777782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:45.143383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Number of Days
Real number (ℝ)

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2337656
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.0 KiB
2024-02-28T13:58:45.463719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum97
Range96
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3778544
Coefficient of variation (CV)1.0445576
Kurtosis116.33974
Mean3.2337656
Median Absolute Deviation (MAD)1
Skewness6.8337792
Sum215178
Variance11.4099
MonotonicityNot monotonic
2024-02-28T13:58:45.690083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 28254
42.5%
3 11506
17.3%
2 6648
 
10.0%
7 6473
 
9.7%
4 3888
 
5.8%
8 2430
 
3.7%
9 2406
 
3.6%
5 2180
 
3.3%
6 2176
 
3.3%
12 47
 
0.1%
Other values (60) 533
 
0.8%
ValueCountFrequency (%)
1 28254
42.5%
2 6648
 
10.0%
3 11506
17.3%
4 3888
 
5.8%
5 2180
 
3.3%
6 2176
 
3.3%
7 6473
 
9.7%
8 2430
 
3.7%
9 2406
 
3.6%
10 39
 
0.1%
ValueCountFrequency (%)
97 2
< 0.1%
96 1
< 0.1%
93 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
89 1
< 0.1%
88 1
< 0.1%
83 1
< 0.1%
82 1
< 0.1%
75 1
< 0.1%
Distinct3748
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size520.0 KiB
Minimum2010-01-03 00:00:00
Maximum2020-04-13 00:00:00
2024-02-28T13:58:45.938560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:46.177855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rooms
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
2
19095 
1
19070 
3
18828 
4
9548 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66541
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 19095
28.7%
1 19070
28.7%
3 18828
28.3%
4 9548
14.3%

Length

2024-02-28T13:58:46.470644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:58:46.665737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 19095
28.7%
1 19070
28.7%
3 18828
28.3%
4 9548
14.3%

Most occurring characters

ValueCountFrequency (%)
2 19095
28.7%
1 19070
28.7%
3 18828
28.3%
4 9548
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66541
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19095
28.7%
1 19070
28.7%
3 18828
28.3%
4 9548
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 66541
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19095
28.7%
1 19070
28.7%
3 18828
28.3%
4 9548
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66541
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19095
28.7%
1 19070
28.7%
3 18828
28.3%
4 9548
14.3%
Distinct614
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-02-28T13:58:47.144182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length30
Mean length15.208172
Min length4

Characters and Unicode

Total characters1011967
Distinct characters61
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel Triton
2nd rowEnchanted Isle
3rd rowSeacoast Hotel
4th rowNight In Paradise
5th rowTiny Digs Hotel
ValueCountFrequency (%)
hotel 18416
 
10.8%
the 13232
 
7.8%
inn 5403
 
3.2%
resort 4887
 
2.9%
motel 4261
 
2.5%
b&b 3279
 
1.9%
suites 2075
 
1.2%
hotels 2028
 
1.2%
1870
 
1.1%
home 1379
 
0.8%
Other values (690) 113158
66.6%
2024-02-28T13:58:48.019183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 116560
 
11.5%
103447
 
10.2%
o 79346
 
7.8%
t 69017
 
6.8%
l 61496
 
6.1%
a 59176
 
5.8%
n 50837
 
5.0%
r 46314
 
4.6%
s 44650
 
4.4%
i 41829
 
4.1%
Other values (51) 339295
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 729924
72.1%
Uppercase Letter 171613
 
17.0%
Space Separator 103447
 
10.2%
Other Punctuation 5232
 
0.5%
Final Punctuation 1097
 
0.1%
Decimal Number 335
 
< 0.1%
Dash Punctuation 319
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 116560
16.0%
o 79346
10.9%
t 69017
9.5%
l 61496
8.4%
a 59176
8.1%
n 50837
 
7.0%
r 46314
 
6.3%
s 44650
 
6.1%
i 41829
 
5.7%
h 23774
 
3.3%
Other values (17) 136925
18.8%
Uppercase Letter
ValueCountFrequency (%)
H 30729
17.9%
T 17285
10.1%
B 16317
9.5%
S 15621
9.1%
R 12470
 
7.3%
M 11184
 
6.5%
C 8489
 
4.9%
I 8048
 
4.7%
P 7542
 
4.4%
A 5585
 
3.3%
Other values (16) 38343
22.3%
Decimal Number
ValueCountFrequency (%)
6 131
39.1%
5 102
30.4%
1 102
30.4%
Other Punctuation
ValueCountFrequency (%)
& 5149
98.4%
. 83
 
1.6%
Space Separator
ValueCountFrequency (%)
103447
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1097
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 901537
89.1%
Common 110430
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 116560
 
12.9%
o 79346
 
8.8%
t 69017
 
7.7%
l 61496
 
6.8%
a 59176
 
6.6%
n 50837
 
5.6%
r 46314
 
5.1%
s 44650
 
5.0%
i 41829
 
4.6%
H 30729
 
3.4%
Other values (43) 301583
33.5%
Common
ValueCountFrequency (%)
103447
93.7%
& 5149
 
4.7%
’ 1097
 
1.0%
- 319
 
0.3%
6 131
 
0.1%
5 102
 
0.1%
1 102
 
0.1%
. 83
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1010776
99.9%
Punctuation 1097
 
0.1%
Latin 1 Sup 94
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 116560
 
11.5%
103447
 
10.2%
o 79346
 
7.9%
t 69017
 
6.8%
l 61496
 
6.1%
a 59176
 
5.9%
n 50837
 
5.0%
r 46314
 
4.6%
s 44650
 
4.4%
i 41829
 
4.1%
Other values (49) 338104
33.4%
Punctuation
ValueCountFrequency (%)
’ 1097
100.0%
Latin 1 Sup
ValueCountFrequency (%)
è 94
100.0%

Hotel Rating
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2885003
Minimum3.3
Maximum4.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.0 KiB
2024-02-28T13:58:48.300049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile3.8
Q14.2
median4.3
Q34.5
95-th percentile4.6
Maximum4.7
Range1.4
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2755163
Coefficient of variation (CV)0.064245373
Kurtosis1.7803154
Mean4.2885003
Median Absolute Deviation (MAD)0.2
Skewness-1.166679
Sum285361.1
Variance0.075909234
MonotonicityNot monotonic
2024-02-28T13:58:48.547650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4.2 12425
18.7%
4.6 10725
16.1%
4.5 9681
14.5%
4.3 9349
14.0%
4.4 8376
12.6%
4.1 6570
9.9%
3.8 4134
 
6.2%
3.9 1355
 
2.0%
3.7 1347
 
2.0%
3.3 1296
 
1.9%
ValueCountFrequency (%)
3.3 1296
 
1.9%
3.7 1347
 
2.0%
3.8 4134
 
6.2%
3.9 1355
 
2.0%
4.1 6570
9.9%
4.2 12425
18.7%
4.3 9349
14.0%
4.4 8376
12.6%
4.5 9681
14.5%
4.6 10725
16.1%
ValueCountFrequency (%)
4.7 1283
 
1.9%
4.6 10725
16.1%
4.5 9681
14.5%
4.4 8376
12.6%
4.3 9349
14.0%
4.2 12425
18.7%
4.1 6570
9.9%
3.9 1355
 
2.0%
3.8 4134
 
6.2%
3.7 1347
 
2.0%

Payment Mode
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Internet Banking
13446 
Debit Card
13363 
Wallet
13317 
Credit Card
13267 
COD
13148 

Length

Max length16
Median length10
Mean length9.22813
Min length3

Characters and Unicode

Total characters614049
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWallet
2nd rowWallet
3rd rowCredit Card
4th rowDebit Card
5th rowWallet

Common Values

ValueCountFrequency (%)
Internet Banking 13446
20.2%
Debit Card 13363
20.1%
Wallet 13317
20.0%
Credit Card 13267
19.9%
COD 13148
19.8%

Length

2024-02-28T13:58:48.814378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:58:49.038800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
card 26630
25.0%
internet 13446
12.6%
banking 13446
12.6%
debit 13363
12.5%
wallet 13317
12.5%
credit 13267
12.4%
cod 13148
12.3%

Most occurring characters

ValueCountFrequency (%)
t 66839
10.9%
e 66839
10.9%
n 53784
8.8%
a 53393
8.7%
r 53343
8.7%
C 53045
8.6%
i 40076
 
6.5%
40076
 
6.5%
d 39897
 
6.5%
l 26634
 
4.3%
Other values (8) 120123
19.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 441060
71.8%
Uppercase Letter 132913
 
21.6%
Space Separator 40076
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 66839
15.2%
e 66839
15.2%
n 53784
12.2%
a 53393
12.1%
r 53343
12.1%
i 40076
9.1%
d 39897
9.0%
l 26634
 
6.0%
g 13446
 
3.0%
k 13446
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
C 53045
39.9%
D 26511
19.9%
I 13446
 
10.1%
B 13446
 
10.1%
W 13317
 
10.0%
O 13148
 
9.9%
Space Separator
ValueCountFrequency (%)
40076
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 573973
93.5%
Common 40076
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 66839
11.6%
e 66839
11.6%
n 53784
9.4%
a 53393
9.3%
r 53343
9.3%
C 53045
9.2%
i 40076
7.0%
d 39897
7.0%
l 26634
 
4.6%
D 26511
 
4.6%
Other values (7) 93612
16.3%
Common
ValueCountFrequency (%)
40076
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 614049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 66839
10.9%
e 66839
10.9%
n 53784
8.8%
a 53393
8.7%
r 53343
8.7%
C 53045
8.6%
i 40076
 
6.5%
40076
 
6.5%
d 39897
 
6.5%
l 26634
 
4.3%
Other values (8) 120123
19.6%

Bank Name
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
United Overseas Bank (UOB)
7500 
Cash
7491 
DBS Bank
7467 
Bank of Singapore (BOS)
7466 
HSBC
7379 
Other values (4)
29238 

Length

Max length26
Median length10
Mean length10.599811
Min length4

Characters and Unicode

Total characters705322
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited Overseas Bank (UOB)
2nd rowEZ-Link
3rd rowGrab
4th rowDBS Paylah
5th rowBank of Singapore (BOS)

Common Values

ValueCountFrequency (%)
United Overseas Bank (UOB) 7500
11.3%
Cash 7491
11.3%
DBS Bank 7467
11.2%
Bank of Singapore (BOS) 7466
11.2%
HSBC 7379
11.1%
DBS Paylah 7330
11.0%
CITI Bank 7322
11.0%
Grab 7304
11.0%
EZ-Link 7282
10.9%

Length

2024-02-28T13:58:49.311865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:58:49.621789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
bank 29755
22.3%
dbs 14797
11.1%
united 7500
 
5.6%
overseas 7500
 
5.6%
uob 7500
 
5.6%
cash 7491
 
5.6%
of 7466
 
5.6%
singapore 7466
 
5.6%
bos 7466
 
5.6%
hsbc 7379
 
5.5%
Other values (4) 29238
21.9%

Most occurring characters

ValueCountFrequency (%)
a 74176
 
10.5%
67017
 
9.5%
B 66897
 
9.5%
n 52003
 
7.4%
S 37108
 
5.3%
k 37037
 
5.3%
e 29966
 
4.2%
s 22491
 
3.2%
O 22466
 
3.2%
r 22270
 
3.2%
Other values (26) 273891
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 356806
50.6%
Uppercase Letter 244285
34.6%
Space Separator 67017
 
9.5%
Open Punctuation 14966
 
2.1%
Close Punctuation 14966
 
2.1%
Dash Punctuation 7282
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 74176
20.8%
n 52003
14.6%
k 37037
10.4%
e 29966
8.4%
s 22491
 
6.3%
r 22270
 
6.2%
i 22248
 
6.2%
o 14932
 
4.2%
h 14821
 
4.2%
v 7500
 
2.1%
Other values (8) 59362
16.6%
Uppercase Letter
ValueCountFrequency (%)
B 66897
27.4%
S 37108
15.2%
O 22466
 
9.2%
C 22192
 
9.1%
U 15000
 
6.1%
D 14797
 
6.1%
I 14644
 
6.0%
H 7379
 
3.0%
P 7330
 
3.0%
T 7322
 
3.0%
Other values (4) 29150
11.9%
Space Separator
ValueCountFrequency (%)
67017
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14966
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14966
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 601091
85.2%
Common 104231
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 74176
 
12.3%
B 66897
 
11.1%
n 52003
 
8.7%
S 37108
 
6.2%
k 37037
 
6.2%
e 29966
 
5.0%
s 22491
 
3.7%
O 22466
 
3.7%
r 22270
 
3.7%
i 22248
 
3.7%
Other values (22) 214429
35.7%
Common
ValueCountFrequency (%)
67017
64.3%
( 14966
 
14.4%
) 14966
 
14.4%
- 7282
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 705322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 74176
 
10.5%
67017
 
9.5%
B 66897
 
9.5%
n 52003
 
7.4%
S 37108
 
5.3%
k 37037
 
5.3%
e 29966
 
4.2%
s 22491
 
3.2%
O 22466
 
3.2%
r 22270
 
3.2%
Other values (26) 273891
38.8%

Booking Price[SGD]
Real number (ℝ)

HIGH CORRELATION 

Distinct491
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.04678
Minimum35
Maximum578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.0 KiB
2024-02-28T13:58:50.190868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile56
Q1125
median199
Q3293
95-th percentile407
Maximum578
Range543
Interquartile range (IQR)168

Descriptive statistics

Standard deviation108.12618
Coefficient of variation (CV)0.50515211
Kurtosis-0.70140534
Mean214.04678
Median Absolute Deviation (MAD)83
Skewness0.38855539
Sum14242887
Variance11691.272
MonotonicityNot monotonic
2024-02-28T13:58:50.452979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
290 630
 
0.9%
160 613
 
0.9%
161 595
 
0.9%
318 565
 
0.8%
158 556
 
0.8%
97 538
 
0.8%
115 516
 
0.8%
289 514
 
0.8%
131 512
 
0.8%
173 491
 
0.7%
Other values (481) 61011
91.7%
ValueCountFrequency (%)
35 62
 
0.1%
36 62
 
0.1%
37 70
 
0.1%
38 193
0.3%
39 72
 
0.1%
40 169
0.3%
41 109
0.2%
42 139
0.2%
43 139
0.2%
44 146
0.2%
ValueCountFrequency (%)
578 2
< 0.1%
576 2
< 0.1%
575 1
< 0.1%
571 1
< 0.1%
568 1
< 0.1%
564 1
< 0.1%
562 1
< 0.1%
558 1
< 0.1%
554 1
< 0.1%
553 1
< 0.1%

Discount
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12546956
Minimum0
Maximum0.25
Zeros2547
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size520.0 KiB
2024-02-28T13:58:50.801037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.06
median0.13
Q30.19
95-th percentile0.24
Maximum0.25
Range0.25
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.075112945
Coefficient of variation (CV)0.59865473
Kurtosis-1.2068243
Mean0.12546956
Median Absolute Deviation (MAD)0.07
Skewness-0.010747477
Sum8348.87
Variance0.0056419545
MonotonicityNot monotonic
2024-02-28T13:58:51.081394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.2 2716
 
4.1%
0.01 2660
 
4.0%
0.09 2650
 
4.0%
0.21 2634
 
4.0%
0.12 2625
 
3.9%
0.22 2624
 
3.9%
0.19 2611
 
3.9%
0.11 2603
 
3.9%
0.23 2595
 
3.9%
0.15 2587
 
3.9%
Other values (16) 40236
60.5%
ValueCountFrequency (%)
0 2547
3.8%
0.01 2660
4.0%
0.02 2495
3.7%
0.03 2482
3.7%
0.04 2577
3.9%
0.05 2543
3.8%
0.06 2512
3.8%
0.07 2473
3.7%
0.08 2435
3.7%
0.09 2650
4.0%
ValueCountFrequency (%)
0.25 2575
3.9%
0.24 2529
3.8%
0.23 2595
3.9%
0.22 2624
3.9%
0.21 2634
4.0%
0.2 2716
4.1%
0.19 2611
3.9%
0.18 2493
3.7%
0.17 2462
3.7%
0.16 2460
3.7%

Taxes (GST)
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0.07
66541 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters266164
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.07
2nd row0.07
3rd row0.07
4th row0.07
5th row0.07

Common Values

ValueCountFrequency (%)
0.07 66541
100.0%

Length

2024-02-28T13:58:51.345724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:58:51.568851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07 66541
100.0%

Most occurring characters

ValueCountFrequency (%)
0 133082
50.0%
. 66541
25.0%
7 66541
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 199623
75.0%
Other Punctuation 66541
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 133082
66.7%
7 66541
33.3%
Other Punctuation
ValueCountFrequency (%)
. 66541
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 266164
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 133082
50.0%
. 66541
25.0%
7 66541
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 266164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 133082
50.0%
. 66541
25.0%
7 66541
25.0%

Profit Margin
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18881652
Minimum0.1
Maximum0.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size520.0 KiB
2024-02-28T13:58:51.806787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.14
median0.2
Q30.23
95-th percentile0.28
Maximum0.3
Range0.2
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.05276504
Coefficient of variation (CV)0.27945139
Kurtosis-0.86690274
Mean0.18881652
Median Absolute Deviation (MAD)0.04
Skewness0.082602761
Sum12564.04
Variance0.0027841494
MonotonicityNot monotonic
2024-02-28T13:58:52.119790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.22 9358
14.1%
0.14 6853
10.3%
0.2 5409
 
8.1%
0.24 5404
 
8.1%
0.25 5356
 
8.0%
0.13 5320
 
8.0%
0.16 4075
 
6.1%
0.1 4041
 
6.1%
0.17 3305
 
5.0%
0.19 3030
 
4.6%
Other values (7) 14390
21.6%
ValueCountFrequency (%)
0.1 4041
6.1%
0.11 2744
4.1%
0.13 5320
8.0%
0.14 6853
10.3%
0.15 2547
 
3.8%
0.16 4075
6.1%
0.17 3305
5.0%
0.18 1243
 
1.9%
0.19 3030
4.6%
0.2 5409
8.1%
ValueCountFrequency (%)
0.3 2702
 
4.1%
0.28 1296
 
1.9%
0.25 5356
8.0%
0.24 5404
8.1%
0.23 2578
 
3.9%
0.22 9358
14.1%
0.21 1280
 
1.9%
0.2 5409
8.1%
0.19 3030
 
4.6%
0.18 1243
 
1.9%

Interactions

2024-02-28T13:58:31.378330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:22.388097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:23.995477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:25.506935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:26.899302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:28.452778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:29.871492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:31.607489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:22.647524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:24.227651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:25.687709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:27.108495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:28.644505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:30.099715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:31.821115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:22.892920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:24.451905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:25.888668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:27.336442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:28.851870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:30.333766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:32.056798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:23.130167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:24.660483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:26.107677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:27.568649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:29.003849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:30.521287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:32.225464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:23.370877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:24.862588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:26.337307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:27.806176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:29.156695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:30.749687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:32.442959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:23.520420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:25.092947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:26.518074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:27.986405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:29.359964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:30.935586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:32.612460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:23.744698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:25.271303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:26.708499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:28.220702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:29.625456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-28T13:58:31.128639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-28T13:58:52.322756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeBank NameBooking Price[SGD]Destination CountryDiscountGenderHotel RatingNo. Of PeopleNumber of DaysOrigin CountryPayment ModeProfit MarginRooms
Age1.0000.012-0.0020.0130.0130.000-0.0020.006-0.0030.0000.0050.0000.008
Bank Name0.0121.000-0.0010.0170.0010.0160.0010.004-0.0050.0000.0080.0020.010
Booking Price[SGD]-0.002-0.0011.0000.010-0.0020.0000.541-0.000-0.0390.0000.000-0.1540.008
Destination Country0.0130.0170.0101.000-0.0060.014-0.002-0.002-0.0060.0050.015-0.0030.006
Discount0.0130.001-0.002-0.0061.0000.0120.0050.0050.0000.0000.009-0.0030.010
Gender0.0000.0160.0000.0140.0121.000-0.003-0.0050.0010.0000.0140.0060.010
Hotel Rating-0.0020.0010.541-0.0020.005-0.0031.000-0.002-0.0080.0000.010-0.3710.011
No. Of People0.0060.004-0.000-0.0020.005-0.005-0.0021.000-0.0030.0010.0060.0061.000
Number of Days-0.003-0.005-0.039-0.0060.0000.001-0.008-0.0031.0000.0000.007-0.0020.002
Origin Country0.0000.0000.0000.0050.0000.0000.0000.0010.0001.0000.0020.0050.000
Payment Mode0.0050.0080.0000.0150.0090.0140.0100.0060.0070.0021.0000.0010.004
Profit Margin0.0000.002-0.154-0.003-0.0030.006-0.3710.006-0.0020.0050.0011.0000.012
Rooms0.0080.0100.0080.0060.0100.0100.0111.0000.0020.0000.0040.0121.000

Missing values

2024-02-28T13:58:33.020477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-28T13:58:34.039629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Booking IDDate of BookingTimeCustomer IDGenderAgeOrigin CountryStateLocationDestination CountryDestination CityNo. Of PeopleCheck-in dateNumber of DaysCheck-Out DateRoomsHotel NameHotel RatingPayment ModeBank NameBooking Price[SGD]DiscountTaxes (GST)Profit Margin
0DDMY000012010-01-0110:49:40MY00001Male42MalaysiaJohorIskandar PuteriDenmarkHorsens12010-01-1282010-01-201Hotel Triton4.3WalletUnited Overseas Bank (UOB)2430.010.070.25
1DDID000022010-01-0109:19:47ID00001Female44IndonesiaCiawiWest JavaColombiaMedellin32010-01-2112010-01-222Enchanted Isle4.2WalletEZ-Link3120.000.070.24
2DDSG000032010-01-0111:52:56SG00001Female31SingaporeCentralRochorGermanyMunich32010-01-0272010-01-092Seacoast Hotel4.5Credit CardGrab3380.190.070.20
3DDSG000042010-01-0113:44:40SG00002Male28SingaporeNorthYishunCanadaMontreal32010-02-1542010-02-192Night In Paradise4.2Debit CardDBS Paylah2540.190.070.13
4DDKH000052010-01-0105:38:26KH00001Male44CambodiaPhnom TropPursatKenyaNairobi52010-01-0332010-01-063Tiny Digs Hotel4.6WalletBank of Singapore (BOS)3130.150.070.17
5DDTH000062010-01-0105:41:55TH00001Male32ThailandChiang MaiChiang MaiCanadaToronto22010-01-2812010-01-291Seascape4.5Internet BankingUnited Overseas Bank (UOB)1610.010.070.15
6DDTH000072010-01-0120:14:21TH00002Male49ThailandSurat ThaniSurat ThaniIranTehran32010-03-1532010-03-182Waldorf Astoria4.6Debit CardHSBC3130.100.070.17
7DDMY000082010-01-0100:22:07MY00002Female58MalaysiaSarawakKuchingEgyptPort Said42010-01-0312010-01-042The Westgate Hotel4.1WalletCash1210.050.070.15
8DDSG000092010-01-0109:30:32SG00003Male57SingaporeNorthMandaiEgyptLuxor42010-04-0932010-04-122Firefly Motel4.4CODEZ-Link1730.200.070.22
9DDSG000102010-01-0116:22:39SG00004Female41SingaporeWestTengahFranceMarseille42010-01-0722010-01-092Cape Grace4.7Credit CardGrab3090.160.070.25
Booking IDDate of BookingTimeCustomer IDGenderAgeOrigin CountryStateLocationDestination CountryDestination CityNo. Of PeopleCheck-in dateNumber of DaysCheck-Out DateRoomsHotel NameHotel RatingPayment ModeBank NameBooking Price[SGD]DiscountTaxes (GST)Profit Margin
66531DDVN665262019-12-3115:27:30VN05957Male52VietnamPhan ThietBinh ThuanIsraelHolon22020-01-0112020-01-021Slumber Falls3.8Debit CardGrab1150.070.070.25
66532DDID665272019-12-3104:18:40ID11986Female30IndonesiaSenayanJakartaIsraelBeersheba42020-03-2812020-03-292Creek Quest3.8CODDBS Paylah1200.000.070.22
66533DDMY665282019-12-3117:53:01MY12048Male46MalaysiaKedahAlor SetarCanadaOttawa52020-01-0132020-01-043Hotel Triton4.1Credit CardHSBC1590.140.070.22
66534DDVN665292019-12-3104:31:42VN05958Female19VietnamDien BanQuang NamMaldivesThinadhoo12020-01-0112020-01-021The Hot Springs Hotel3.8Internet BankingBank of Singapore (BOS)1160.140.070.22
66535DDMY665302019-12-3104:15:03MY12049Male46MalaysiaJohorJohor BahruIcelandReykjavik32020-01-0912020-01-102Hotel The Pie4.2Debit CardDBS Bank1810.150.070.10
66536DDSG665312019-12-3123:36:16SG12034Female42SingaporeCentralOrchardGermanyBerlin42020-01-0642020-01-102Silver Cloud Inn4.3WalletGrab1820.230.070.24
66537DDSG665322019-12-3114:41:01SG12035Female54SingaporeCentralGeylangIsraelHolon42020-04-0942020-04-132The Elet4.2CODDBS Paylah1250.060.070.19
66538DDSG665332019-12-3119:11:16SG12036Female57SingaporeCentralDowntown CoreCanadaOttawa72020-01-0912020-01-104The Elet4.4Debit CardEZ-Link3180.020.070.22
66539DDTH665342019-12-3105:12:29TH12170Female44ThailandSurat ThaniKo SamuiMaldivesViligili32020-01-0112020-01-022Sunset Lodge4.2Debit CardHSBC1730.140.070.25
66540DDVN665352019-12-3100:51:52VN05959Female52VietnamPleikuGia LaiEgyptLuxor52020-01-2442020-01-283Coastal bay hotel4.3Internet BankingGrab1820.170.070.24

Duplicate rows

Most frequently occurring

Booking IDDate of BookingCustomer IDGenderAgeOrigin CountryStateLocationDestination CountryDestination CityNo. Of PeopleCheck-in dateNumber of DaysCheck-Out DateRoomsHotel NameHotel RatingPayment ModeBank NameBooking Price[SGD]DiscountTaxes (GST)Profit Margin# duplicates
3DDVN343402016-02-23VN03010Male38VietnamPleikuGia LaiItalyRome42016-03-1942016-03-232Hotel Lucia4.4Credit CardHSBC1700.100.070.223
0DDKH507382018-04-14KH04520Male46CambodiaKrang YovKandalNew ZealandDunedin42018-04-2372018-04-302The Fresco Hotel3.8Internet BankingEZ-Link990.060.070.222
1DDMY000082010-01-01MY00002Female58MalaysiaSarawakKuchingEgyptPort Said42010-01-0312010-01-042The Westgate Hotel4.1WalletCash1210.050.070.152
2DDSG225372014-03-23SG04062Male41SingaporeCentralGeylangGermanyEssen22014-03-2412014-03-251Royal Orbit4.2WalletHSBC480.170.070.192